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Automatic string detection for bass guitar and electric guitar

: Abeßer, Jakob

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Queen Mary University of London -QMUL-, Centre for Digital Music; Centre National de la Recherche Scientifique -CNRS-, Laboratoire de Mécanique et D'Acoustique -LMA-, Marseille:
Music and emotions. Proceedings : 9th International Symposium on Computer Music Modeling and Retrieval, CMMR 2012. 19-22 June, Queen Mary University of London, London, UK
London, 2012
International Symposium on Computer Music Modeling and Retrieval (CMMR) <9, 2012, London>
Conference Paper, Electronic Publication
Fraunhofer IDMT ()

In this paper, we present a feature-based approach to automatically estimate the string number in recordings of the bass guitar and the electric guitar. We perform different experiments to evaluate the classification performance on isolated note recordings. First, we analyze how factors such as the instrument, the playing style, and the pick-up settings affect the performance of the classification system. Second, we investigate, how the classification performance can be improved by rejecting implausible classifications as well as aggregating the classification results over multiple adjacent time frames. The best results we obtained are f-measure values of F = .93 for the bass guitar (4 classes) and F = .90 for the electric guitar (6 classes).